Cong Lu

Cong Lu

Deep Reinforcement Learning, Meta-Learning, Bayesian Optimisation

I am a DPhil student supervised by Michael A. Osborne and Yee Whye Teh. My research interests span deep reinforcement learning, meta-learning and Bayesian Optimisation. I am particularly interested in offline reinforcement learning (including generalisation to new tasks and uncertainty quantification for pessimistic MDPs) and reinforcement learning as probabilistic inference. I obtained my undergraduate degree in Mathematics and Computer Science from the University of Oxford.



  • P. J. Ball , C. Lu , J. Parker-Holder , S. Roberts , Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment, International Conference on Machine Learning, 2021.
  • X. Wan , V. Nguyen , H. Ha , B. Ru , C. Lu , M. A. Osborne , Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces, International Conference on Machine Learning, 2021.
  • L. Zintgraf , L. Feng , C. Lu , M. Igl , K. Hartikainen , K. Hofmann , S. Whiteson , Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning, International Conference on Machine Learning, 2021.
  • T. G. J. Rudner , C. Lu , M. A. Osborne , Y. Gal , Y. W. Teh , On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations, ICLR 2021 RobustML Workshop, 2021.